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Diagnostic Checking in a Flexible Nonlinear Time Series Model

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Author Info

  • Medeiros, Marcelo

    ()
    (Dept. of Economic Statistics, Stockholm School of Economics)

  • Veiga, Alvaro

    ()
    (Dept. of Electrical Engineering, Catholic University of Rio de Janeiro)

Abstract

This paper considers a sequence of misspecification tests for a flexible nonlinear time series model. The model is a generalization of both the Smooth Transition AutoRegressive (STAR) and the AutoRegressive Artificial Artificial Neural Network (AR-ANN) models. The tests are Lagrange multiplier (LM) type tests of parameter constancy against the alternative of smoothly changing ones, of serial independence, and constant variance of the error term against the hypothesis that the variance smoothly changes between regimes. The small sample behaviour of the proposed tests is evaluated throw a Monte-Carlo study and the results show that the tests have size close to the nominal one and a good power.

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Bibliographic Info

Paper provided by Stockholm School of Economics in its series Working Paper Series in Economics and Finance with number 386.

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Length: 24 pages
Date of creation: 06 Jun 2000
Date of revision: 15 Jan 2001
Publication status: Published in Journal of Time Series Analysis, 2003, pages 461-482.
Handle: RePEc:hhs:hastef:0386

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Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden
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Web page: http://www.hhs.se/
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Keywords: Time series; nonlinear models; STAR models; neural networks; statistical inference; parameter constancy; serial independence; heteroscedasticity; misspecification;

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References

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  1. Davidson, Russell & MacKinnon, James G, 1998. "Graphical Methods for Investigating the Size and Power of Hypothesis Tests," The Manchester School of Economic & Social Studies, University of Manchester, vol. 66(1), pages 1-26, January.
  2. Lundbergh, Stefan & Terasvirta, Timo & van Dijk, Dick, 2003. "Time-Varying Smooth Transition Autoregressive Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 104-21, January.
  3. Chung-Ming Kuan, 2006. "Artificial Neural Networks," IEAS Working Paper : academic research 06-A010, Institute of Economics, Academia Sinica, Taipei, Taiwan.
  4. Granger, Clive W. J. & Terasvirta, Timo, 1993. "Modelling Non-Linear Economic Relationships," OUP Catalogue, Oxford University Press, number 9780198773207, Octomber.
  5. Breusch, T S & Pagan, A R, 1980. "The Lagrange Multiplier Test and Its Applications to Model Specification in Econometrics," Review of Economic Studies, Wiley Blackwell, vol. 47(1), pages 239-53, January.
  6. Eitrheim, Oyvind & Terasvirta, Timo, 1996. "Testing the adequacy of smooth transition autoregressive models," Journal of Econometrics, Elsevier, vol. 74(1), pages 59-75, September.
  7. Medeiros, Marcelo & Veiga, Alvaro, 2000. "A Flexible Coefficient Smooth Transition Time Series Model," Working Paper Series in Economics and Finance 360, Stockholm School of Economics, revised 10 Feb 2000.
  8. Rech, Gianluigi & Teräsvirta, Timo & Tschernig, Rolf, 1999. "A simple variable selection technique for nonlinear models," SFB 373 Discussion Papers 1999,26, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
  9. Breusch, T S & Pagan, A R, 1979. "A Simple Test for Heteroscedasticity and Random Coefficient Variation," Econometrica, Econometric Society, vol. 47(5), pages 1287-94, September.
  10. repec:wop:humbsf:1999-26 is not listed on IDEAS
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Cited by:
  1. Dimitris K. Christopoulos & Miguel León-Ledesma, 2004. "Current Account Sustainability in the US: What Do We Really Know About It?," Studies in Economics 0412, Department of Economics, University of Kent.
  2. Marie Lebreton & Katia Melnik, 2009. "Voluntary Participation as a Determinant of Social Capital in France : Allowing for Parameter Heterogeneity," Working Papers halshs-00410530, HAL.
  3. Eklund, Bruno & Teräsvirta, Timo, 2003. "Testing constancy of the error covariance matrix in vector models," Working Paper Series in Economics and Finance 549, Stockholm School of Economics, revised 18 Jan 2006.
  4. Michael McAller & Marcelo C. Medeiros, 2007. "A multiple regime smooth transition heterogeneous autoregressive model for long memory and asymmetries," Textos para discussão 544, Department of Economics PUC-Rio (Brazil).
  5. ERIC HILLEBRAND & MArcelo Cunha Medeiros, 2010. "Asymmetries, breaks, and long-range dependence: An estimation framework for daily realized volatility," Textos para discussão 578, Department of Economics PUC-Rio (Brazil).
  6. Jonathan B. Hill, 2004. "Consistent and Non-Degenerate Model Specification Tests Against Smooth Transition Alternatives," Working Papers 0406, Florida International University, Department of Economics.
  7. Alberto Musso & Livio Stracca & Dick van Dijk, 2009. "Instability and Nonlinearity in the Euro-Area Phillips Curve," International Journal of Central Banking, International Journal of Central Banking, vol. 5(2), pages 181-212, June.
  8. Yukai Yang, 2014. "Testing Constancy of the Error Covariance Matrix in Vector Models against Parametric Alternatives using a Spectral Decomposition," CREATES Research Papers 2014-11, School of Economics and Management, University of Aarhus.
  9. Scharth, Marcel & Medeiros, Marcelo C., 2009. "Asymmetric effects and long memory in the volatility of Dow Jones stocks," International Journal of Forecasting, Elsevier, vol. 25(2), pages 304-327.

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